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sdedit project page sdedit guided image synthesis and editing with stochastic differential equations chenlin meng yutong he yang song jiaming song jiajun wu jun yan zhu stefano ermon stanford university carnegie mellon university in iclr 2022 paper github colab sdedit is an image synthesis and editing framework based on stochastic differential equations sdes or diffusion models sdedit allows stroke based image synthesis stroke based image editing and image compositing without task specific optimization sdedit can be directly plugged into off the shelf pre trained score based or diffusion models recently sdedit has also been applied to text guided image editing with large scale text to image models notable examples include stable diffusion s img2img function see here glide and distilled sd abstract guided image synthesis enables everyday users to create and edit photo realistic images with minimum effort the key challenge is balancing faithfulness to the user input e g hand drawn colored strokes and realism of the synthesized image existing gan based methods attempt to achieve such balance using either conditional gans or gan inversions which are challenging and often require additional training data or loss functions for individual applications to address these issues we introduce a new image synthesis and editing method stochastic differential editing sdedit based on a diffusion model generative prior which synthesizes realistic images by iteratively denoising through a stochastic differential equation sde given an input image with user guide of any type sdedit first adds noise to the input then subsequently denoises the resulting image through the sde prior to increase its realism sdedit does not require task specific training or inversions and can naturally achieve the balance between realism and faithfulness sdedit significantly outperforms state of the art gan based methods by up to 98 09 on realism and 91 72 on overall satisfaction scores according to a human perception study on multiple tasks including stroke based image synthesis and editing as well as image compositing paper arxiv 2108 01073 2021 citation chenlin meng yutong he yang song jiaming song jiajun wu jun yan zhu and stefano ermon sdedit guided image synthesis and editing with stochastic differential equations in international conference on learning representations iclr 2022 bibtex introducing sdedit a powerful image synthesis and editing technique the key intuition of sdedit is to hijack the reverse stochastic process of sde based generative models as illustrated in the figure below given an input image for editing such as a stroke painting or an image with strokes we can add a suitable amount of noise to make its artifacts undetectable while still preserving the overall structure of the image we then initialize the reverse sde with this noisy input and simulate the reverse process to obtain a denoised image of high quality because the denoised image and the input resembles each other with noise perturbations they also share the overall image structure synthesizing images from strokes with sdedit given an input stroke painting our goal is to generate a realistic image that shares the same structure as the input when no paired data is available we present stroke based image synthesis with sdedit on lsun bedroom lsun church and celeba hq datasets we notice that sdedit can generate multiple diverse images for each stroke painting scribble based image editing with sdedit given an input with user added strokes we want to generate a realistic image based on the user s edit we observe that our method can generate image edits that are both realistic and faithful to the user edit while avoid making undesired modifications see the figure below image compositing with sdedit given an image users can specify how they want the edited image to look like using pixel patches copied from other reference images our goal is to generate a realistic image based on the user s edit in the figure below original stands for the orignal image and input stands for an input designed by users we observe that sdedit can generate both faithful and realistic images with much lower lpips scores compared to gan baselines related work song yang jascha sohl dickstein diederik p kingma abhishek kumar stefano ermon and ben poole score based generative modeling through stochastic differential equations iclr 2021 song jiaming chenlin meng and stefano ermon denoising diffusion implicit models iclr 2021 song yang and stefano ermon generative modeling by estimating gradients of the data distribution neurips 2019 song yang and stefano ermon improved techniques for training score based generative models neurips 2020 ho jonathan ajay jain and pieter abbeel denoising diffusion probabilistic models neurips 2020